Overview

Dataset statistics

Number of variables11
Number of observations181
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory52.7 B

Variable types

TimeSeries11

Alerts

Active Truck Utilization (SA) is highly overall correlated with Total TL: Spot Rate (exc. FSC, SA)High correlation
Total Truck Loadings (SA) is highly overall correlated with Total TL: Spot Rate (exc. FSC, SA) and 5 other fieldsHigh correlation
Total TL: Spot Rate (exc. FSC, SA) is highly overall correlated with Active Truck Utilization (SA) and 6 other fieldsHigh correlation
Total TL: Contract Rate (exc. FSC, SA) is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
Driver Labor Index (1992=100, SA) is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
Real GDP is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
CPI Index is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
3 Month T-Bill Rate, % is highly overall correlated with Driver Labor Index (1992=100, SA)High correlation
Truck Transportation Employment (000's) is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
Class 8 Truck Net Orders, US/CAN is highly overall correlated with Total TL: Spot Rate (exc. FSC, SA)High correlation
Total Truck Loadings (SA) is non stationaryNon stationary
Total TL: Spot Rate (exc. FSC, SA) is non stationaryNon stationary
Total TL: Contract Rate (exc. FSC, SA) is non stationaryNon stationary
Real GDP is non stationaryNon stationary
CPI Index is non stationaryNon stationary
3 Month T-Bill Rate, % is non stationaryNon stationary
National Avg. Diesel Fuel Price ($/Gal.) is non stationaryNon stationary
Truck Transportation Employment (000's) is non stationaryNon stationary
Total Truck Loadings (SA) has unique valuesUnique
Total TL: Spot Rate (exc. FSC, SA) has unique valuesUnique
Total TL: Contract Rate (exc. FSC, SA) has unique valuesUnique
Driver Labor Index (1992=100, SA) has unique valuesUnique
Real GDP has unique valuesUnique

Reproduction

Analysis started2023-05-02 05:05:25.971421
Analysis finished2023-05-02 05:05:44.951294
Duration18.98 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Distinct170
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91222736
Minimum0.8131572
Maximum1
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:45.034436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.8131572
5-th percentile0.84645677
Q10.87714231
median0.89481217
Q30.95421231
95-th percentile1
Maximum1
Range0.1868428
Interquartile range (IQR)0.077069998

Descriptive statistics

Standard deviation0.050842408
Coefficient of variation (CV)0.05573436
Kurtosis-0.92559934
Mean0.91222736
Median Absolute Deviation (MAD)0.031892002
Skewness0.43264502
Sum165.11315
Variance0.0025849505
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0099444383
2023-05-01T22:05:45.223538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
6.6%
0.8836542964 1
 
0.6%
0.9982427359 1
 
0.6%
0.9873948693 1
 
0.6%
0.9842237234 1
 
0.6%
0.9907720685 1
 
0.6%
0.9988526106 1
 
0.6%
0.9996961951 1
 
0.6%
0.9981672764 1
 
0.6%
0.9991195202 1
 
0.6%
Other values (160) 160
88.4%
ValueCountFrequency (%)
0.8131572008 1
0.6%
0.8194220662 1
0.6%
0.8230690956 1
0.6%
0.8236809969 1
0.6%
0.8274359703 1
0.6%
0.8336769938 1
0.6%
0.8338410258 1
0.6%
0.8424488306 1
0.6%
0.845013082 1
0.6%
0.8464567661 1
0.6%
ValueCountFrequency (%)
1 12
6.6%
0.9996961951 1
 
0.6%
0.99931252 1
 
0.6%
0.9991195202 1
 
0.6%
0.9988526106 1
 
0.6%
0.9982427359 1
 
0.6%
0.9981672764 1
 
0.6%
0.995908618 1
 
0.6%
0.995757699 1
 
0.6%
0.9944194555 1
 
0.6%
2023-05-01T22:05:45.388146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total Truck Loadings (SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57292195
Minimum46922830
Maximum66325987
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2023-05-01T22:05:45.656372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum46922830
5-th percentile48668342
Q151887366
median55529202
Q363682838
95-th percentile65222142
Maximum66325987
Range19403157
Interquartile range (IQR)11795473

Descriptive statistics

Standard deviation5797451.5
Coefficient of variation (CV)0.10119095
Kurtosis-1.5099328
Mean57292195
Median Absolute Deviation (MAD)4851118.9
Skewness0.055776298
Sum1.0369887 × 1010
Variance3.3610444 × 1013
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9055944385
2023-05-01T22:05:45.814311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57726252.49 1
 
0.6%
62587189.4 1
 
0.6%
62668353.26 1
 
0.6%
63633368.7 1
 
0.6%
63851471.32 1
 
0.6%
63411577.66 1
 
0.6%
62371633.72 1
 
0.6%
64220316.17 1
 
0.6%
63729121.98 1
 
0.6%
63777050.88 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
46922829.7 1
0.6%
47399646.47 1
0.6%
47755672.42 1
0.6%
47848746.95 1
0.6%
48199115.22 1
0.6%
48348978.07 1
0.6%
48461202.56 1
0.6%
48637349.24 1
0.6%
48653069.63 1
0.6%
48668341.89 1
0.6%
ValueCountFrequency (%)
66325987.05 1
0.6%
66026843.94 1
0.6%
65902908.92 1
0.6%
65778118.88 1
0.6%
65743784.02 1
0.6%
65670664.36 1
0.6%
65492406.67 1
0.6%
65474946.72 1
0.6%
65463696.01 1
0.6%
65222141.66 1
0.6%
2023-05-01T22:05:45.959960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total TL: Spot Rate (exc. FSC, SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.34289
Minimum76.620911
Maximum169.92357
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:46.291157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum76.620911
5-th percentile83.417725
Q1101.61458
median107.49603
Q3117.95395
95-th percentile153.43565
Maximum169.92357
Range93.302658
Interquartile range (IQR)16.339371

Descriptive statistics

Standard deviation18.551622
Coefficient of variation (CV)0.16661704
Kurtosis1.1155282
Mean111.34289
Median Absolute Deviation (MAD)7.579834
Skewness0.96822834
Sum20153.062
Variance344.16266
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.09828346684
2023-05-01T22:05:46.429974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97.7772522 1
 
0.6%
111.2966766 1
 
0.6%
122.4535141 1
 
0.6%
124.2550278 1
 
0.6%
122.1006546 1
 
0.6%
125.996254 1
 
0.6%
136.1178589 1
 
0.6%
128.7919312 1
 
0.6%
128.7690277 1
 
0.6%
128.0793762 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
76.62091064 1
0.6%
76.86597443 1
0.6%
78.0898819 1
0.6%
78.9952774 1
0.6%
79.02855682 1
0.6%
79.89736176 1
0.6%
80.00791931 1
0.6%
80.63197327 1
0.6%
80.69255829 1
0.6%
83.41772461 1
0.6%
ValueCountFrequency (%)
169.9235687 1
0.6%
168.110321 1
0.6%
159.8557434 1
0.6%
158.7514038 1
0.6%
158.5955963 1
0.6%
158.3710632 1
0.6%
157.5389404 1
0.6%
155.1854706 1
0.6%
154.3363342 1
0.6%
153.4356537 1
0.6%
2023-05-01T22:05:46.562597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total TL: Contract Rate (exc. FSC, SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.30031
Minimum95.407272
Maximum157.50975
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:46.824229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum95.407272
5-th percentile96.354103
Q1104.78954
median113.2218
Q3125.51294
95-th percentile150.57887
Maximum157.50975
Range62.102478
Interquartile range (IQR)20.723396

Descriptive statistics

Standard deviation16.11372
Coefficient of variation (CV)0.13855269
Kurtosis0.096110292
Mean116.30031
Median Absolute Deviation (MAD)10.944962
Skewness0.9096275
Sum21050.355
Variance259.65198
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.79078535
2023-05-01T22:05:46.971356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0452347 1
 
0.6%
115.0490723 1
 
0.6%
116.9460144 1
 
0.6%
118.7285538 1
 
0.6%
120.5640869 1
 
0.6%
122.0886383 1
 
0.6%
122.8691635 1
 
0.6%
122.9025497 1
 
0.6%
124.2307587 1
 
0.6%
125.0918121 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
95.40727234 1
0.6%
95.4593811 1
0.6%
95.47031403 1
0.6%
95.8232193 1
0.6%
95.92572021 1
0.6%
96.0970993 1
0.6%
96.1157608 1
0.6%
96.14700317 1
0.6%
96.35186005 1
0.6%
96.35410309 1
0.6%
ValueCountFrequency (%)
157.5097504 1
0.6%
157.2530365 1
0.6%
157.2195129 1
0.6%
156.1516876 1
0.6%
155.6170349 1
0.6%
153.7172546 1
0.6%
153.644455 1
0.6%
153.0596924 1
0.6%
152.1927948 1
0.6%
150.5788727 1
0.6%
2023-05-01T22:05:47.122174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Driver Labor Index (1992=100, SA)
Numeric time series

HIGH CORRELATION  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.75601
Minimum116.6245
Maximum128.39658
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:47.392283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum116.6245
5-th percentile119.78436
Q1124.39847
median125.13635
Q3125.62083
95-th percentile126.82518
Maximum128.39658
Range11.772072
Interquartile range (IQR)1.2223587

Descriptive statistics

Standard deviation2.0061133
Coefficient of variation (CV)0.016080294
Kurtosis6.3668342
Mean124.75601
Median Absolute Deviation (MAD)0.63317871
Skewness-2.2848051
Sum22580.837
Variance4.0244904
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001106261993
2023-05-01T22:05:47.536516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.6429672 1
 
0.6%
125.4240875 1
 
0.6%
125.8094482 1
 
0.6%
125.5935898 1
 
0.6%
125.1363525 1
 
0.6%
125.1832428 1
 
0.6%
125.3566055 1
 
0.6%
125.6441345 1
 
0.6%
125.9879837 1
 
0.6%
125.9078979 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
116.6245041 1
0.6%
116.6429672 1
0.6%
116.7327957 1
0.6%
117.1516495 1
0.6%
117.6040802 1
0.6%
118.14888 1
0.6%
118.4851685 1
0.6%
118.896019 1
0.6%
119.283493 1
0.6%
119.7843628 1
0.6%
ValueCountFrequency (%)
128.3965759 1
0.6%
127.8454971 1
0.6%
127.6487808 1
0.6%
127.566597 1
0.6%
127.3815231 1
0.6%
127.3434219 1
0.6%
127.2948761 1
0.6%
127.0305481 1
0.6%
126.8834457 1
0.6%
126.8251801 1
0.6%
2023-05-01T22:05:47.684501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Real GDP
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17421.619
Minimum15160.878
Maximum20260.513
Zeros0
Zeros (%)0.0%
Memory size1.5 KiB
2023-05-01T22:05:47.942695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum15160.878
5-th percentile15267.662
Q115976.762
median17406.874
Q318726.462
95-th percentile19985.116
Maximum20260.513
Range5099.6357
Interquartile range (IQR)2749.7

Descriptive statistics

Standard deviation1516.5033
Coefficient of variation (CV)0.087047211
Kurtosis-1.1880369
Mean17421.619
Median Absolute Deviation (MAD)1350.9936
Skewness0.22466165
Sum3153313
Variance2299782.3
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9868275801
2023-05-01T22:05:48.089178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15711.13191 1
 
0.6%
18068.19123 1
 
0.6%
18190.90274 1
 
0.6%
18255.47879 1
 
0.6%
18313.90898 1
 
0.6%
18361.62865 1
 
0.6%
18401.81549 1
 
0.6%
18436.7925 1
 
0.6%
18472.74064 1
 
0.6%
18514.24455 1
 
0.6%
Other values (171) 171
94.5%
ValueCountFrequency (%)
15160.87757 1
0.6%
15161.75337 1
0.6%
15162.71487 1
0.6%
15167.43739 1
0.6%
15174.95464 1
0.6%
15182.28177 1
0.6%
15208.9866 1
0.6%
15212.20327 1
0.6%
15267.64485 1
0.6%
15267.66206 1
0.6%
ValueCountFrequency (%)
20260.51325 1
0.6%
20220.68598 1
0.6%
20181.4563 1
0.6%
20145.29735 1
0.6%
20107.27019 1
0.6%
20059.78078 1
0.6%
20045.96621 1
0.6%
20038.79284 1
0.6%
19998.63504 1
0.6%
19985.11589 1
0.6%
2023-05-01T22:05:48.232868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CPI Index
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct179
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.88482
Minimum211.39799
Maximum300.53601
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:48.489259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum211.39799
5-th percentile213.448
Q1226.75
median237.498
Q3255.159
95-th percentile288.61099
Maximum300.53601
Range89.138016
Interquartile range (IQR)28.408997

Descriptive statistics

Standard deviation21.552267
Coefficient of variation (CV)0.089101362
Kurtosis0.33922854
Mean241.88482
Median Absolute Deviation (MAD)14.684006
Skewness0.84850061
Sum43781.153
Variance464.50018
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.999088027
2023-05-01T22:05:48.633292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
224.8059998 2
 
1.1%
237.4980011 2
 
1.1%
212.173996 1
 
0.6%
251.0180054 1
 
0.6%
246.6260071 1
 
0.6%
247.2839966 1
 
0.6%
247.8049927 1
 
0.6%
248.8589935 1
 
0.6%
249.529007 1
 
0.6%
249.5769958 1
 
0.6%
Other values (169) 169
93.4%
ValueCountFrequency (%)
211.397995 1
0.6%
211.9329987 1
0.6%
212.173996 1
0.6%
212.4949951 1
0.6%
212.6869965 1
0.6%
212.7050018 1
0.6%
212.7089996 1
0.6%
213.0220032 1
0.6%
213.1529999 1
0.6%
213.447998 1
0.6%
ValueCountFrequency (%)
300.5360107 1
0.6%
298.9899902 1
0.6%
298.5979919 1
0.6%
297.9869995 1
0.6%
296.5390015 1
0.6%
295.3200073 1
0.6%
294.7279968 1
0.6%
294.6279907 1
0.6%
291.2680054 1
0.6%
288.6109924 1
0.6%
2023-05-01T22:05:48.773016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

3 Month T-Bill Rate, %
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct177
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66273746
Minimum0.011428571
Maximum4.5374999
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:49.031350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.011428571
5-th percentile0.017999999
Q10.052380953
median0.1495
Q31.0747619
95-th percentile2.3828571
Maximum4.5374999
Range4.5260713
Interquartile range (IQR)1.0223809

Descriptive statistics

Standard deviation0.94764322
Coefficient of variation (CV)1.4298923
Kurtosis2.8654122
Mean0.66273746
Median Absolute Deviation (MAD)0.12177272
Skewness1.7590765
Sum119.95548
Variance0.89802772
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4098213844
2023-05-01T22:05:49.178237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05454545468 2
 
1.1%
0.07428571582 2
 
1.1%
0.05238095298 2
 
1.1%
0.05999999866 2
 
1.1%
0.9827272892 1
 
0.6%
1.014347792 1
 
0.6%
1.029500008 1
 
0.6%
1.074761868 1
 
0.6%
1.229523778 1
 
0.6%
1.322000027 1
 
0.6%
Other values (167) 167
92.3%
ValueCountFrequency (%)
0.01142857131 1
0.6%
0.01380952355 1
0.6%
0.01400000043 1
0.6%
0.01499999966 1
0.6%
0.01523809507 1
0.6%
0.01600000076 1
0.6%
0.01649999991 1
0.6%
0.01681818254 1
0.6%
0.01789473742 1
0.6%
0.01799999923 1
0.6%
ValueCountFrequency (%)
4.537499905 1
0.6%
4.252380848 1
0.6%
4.151500225 1
0.6%
3.717000008 1
0.6%
3.126666784 1
0.6%
2.75333333 1
0.6%
2.630869627 1
0.6%
2.402380943 1
0.6%
2.388421059 1
0.6%
2.382857084 1
0.6%
2023-05-01T22:05:49.320593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF
Distinct173
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3111602
Minimum1.998
Maximum5.7540002
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:49.580975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.998
5-th percentile2.3099999
Q12.681
median3.1300001
Q33.885
95-th percentile4.7140002
Maximum5.7540002
Range3.7560002
Interquartile range (IQR)1.204

Descriptive statistics

Standard deviation0.77674788
Coefficient of variation (CV)0.23458481
Kurtosis0.24972875
Mean3.3111602
Median Absolute Deviation (MAD)0.62000012
Skewness0.71099705
Sum599.32
Variance0.60333729
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1503295822
2023-05-01T22:05:49.737690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.904999971 3
 
1.7%
2.595000029 2
 
1.1%
2.996999979 2
 
1.1%
2.90899992 2
 
1.1%
3.069000006 2
 
1.1%
3.960999966 2
 
1.1%
2.785000086 2
 
1.1%
3.36500001 1
 
0.6%
3.299999952 1
 
0.6%
3.122999907 1
 
0.6%
Other values (163) 163
90.1%
ValueCountFrequency (%)
1.998000026 1
0.6%
2.089999914 1
0.6%
2.092000008 1
0.6%
2.142999887 1
0.6%
2.15199995 1
0.6%
2.194999933 1
0.6%
2.220000029 1
0.6%
2.226999998 1
0.6%
2.292000055 1
0.6%
2.309999943 1
0.6%
ValueCountFrequency (%)
5.754000187 1
0.6%
5.571000099 1
0.6%
5.486000061 1
0.6%
5.255000114 1
0.6%
5.210999966 1
0.6%
5.119999886 1
0.6%
5.105000019 1
0.6%
5.013000011 1
0.6%
4.993000031 1
0.6%
4.714000225 1
0.6%
2023-05-01T22:05:49.885726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Truck Transportation Employment (000's)
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct146
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1422.2486
Minimum1235
Maximum1615
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:50.153029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1235
5-th percentile1251
Q11356
median1444
Q31492
95-th percentile1572
Maximum1615
Range380
Interquartile range (IQR)136

Descriptive statistics

Standard deviation96.706886
Coefficient of variation (CV)0.067995767
Kurtosis-0.69800842
Mean1422.2486
Median Absolute Deviation (MAD)68
Skewness-0.24050076
Sum257427
Variance9352.2217
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.952264966
2023-05-01T22:05:50.304625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1443 3
 
1.7%
1452 3
 
1.7%
1444 3
 
1.7%
1456 3
 
1.7%
1521 3
 
1.7%
1445 3
 
1.7%
1392 3
 
1.7%
1383 3
 
1.7%
1492 2
 
1.1%
1533 2
 
1.1%
Other values (136) 153
84.5%
ValueCountFrequency (%)
1235 1
0.6%
1236 1
0.6%
1238 1
0.6%
1239 1
0.6%
1241 1
0.6%
1243 1
0.6%
1246 1
0.6%
1247 2
1.1%
1251 1
0.6%
1252 1
0.6%
ValueCountFrequency (%)
1615 1
0.6%
1611 1
0.6%
1606 1
0.6%
1605 1
0.6%
1600 1
0.6%
1598 1
0.6%
1597 1
0.6%
1591 1
0.6%
1584 1
0.6%
1572 1
0.6%
2023-05-01T22:05:50.454210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF
Distinct180
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19765.547
Minimum3688
Maximum50632
Zeros0
Zeros (%)0.0%
Memory size852.0 B
2023-05-01T22:05:50.725569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3688
5-th percentile7473
Q112574
median17915
Q323182
95-th percentile41415
Maximum50632
Range46944
Interquartile range (IQR)10608

Descriptive statistics

Standard deviation10378.994
Coefficient of variation (CV)0.52510533
Kurtosis0.8991437
Mean19765.547
Median Absolute Deviation (MAD)5341
Skewness1.1465632
Sum3577564
Variance1.0772353 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.001700216138
2023-05-01T22:05:50.869914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19387 2
 
1.1%
16185 1
 
0.6%
18728 1
 
0.6%
33802 1
 
0.6%
30785 1
 
0.6%
34121 1
 
0.6%
46259 1
 
0.6%
36934 1
 
0.6%
43304 1
 
0.6%
32412 1
 
0.6%
Other values (170) 170
93.9%
ValueCountFrequency (%)
3688 1
0.6%
4525 1
0.6%
5818 1
0.6%
6039 1
0.6%
6241 1
0.6%
6854 1
0.6%
6962 1
0.6%
7043 1
0.6%
7338 1
0.6%
7473 1
0.6%
ValueCountFrequency (%)
50632 1
0.6%
50407 1
0.6%
49869 1
0.6%
49818 1
0.6%
47983 1
0.6%
46259 1
0.6%
43566 1
0.6%
43304 1
0.6%
41525 1
0.6%
41415 1
0.6%
2023-05-01T22:05:51.009557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Interactions

2023-05-01T22:05:43.090246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:26.419268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:27.964525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.452315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:30.903301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.421030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:33.931336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.451247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:36.895536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:38.354099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:41.551201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:43.232109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:26.590375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:28.101898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.592008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:31.057355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.560576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:34.072412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.590055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.033506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:38.502224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:41.701699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:43.372710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:26.730127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:28.241595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.732249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:31.206179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.696976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:34.216274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.733567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.179689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:38.659749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:41.845694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:43.499130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:26.856686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:28.368530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.854831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:31.338153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.823818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:34.350786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.859129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.303351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:38.789461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:41.977493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:43.633922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:26.984684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:28.508461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.984046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:31.476677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.958653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:34.495079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.994959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.435329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:38.934208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:42.121761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:43.766638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:27.126797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:28.644939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:30.130586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:31.613101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:33.096694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:34.629244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:36.123128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.566804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:39.071842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:42.258030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:43.896298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:27.270593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:28.775913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:30.257418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:31.748922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:33.235374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:34.781303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:36.251483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.691677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:39.232951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:42.398087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:44.017275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:27.402891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:28.904541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:30.382192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:31.874978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:33.368845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:34.909181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:36.369557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.818068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:39.366081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:42.528119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:44.136813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:27.533117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.034334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:30.504811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.005068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:33.503285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.039845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:36.490189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:37.942841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:39.498316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:42.660424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:44.275971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:27.675375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.181461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:30.645656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.148284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:33.649871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.182716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:36.629947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:38.086414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:39.645115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:42.810797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:44.417378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:27.830667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:29.319395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:30.779677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:32.293117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:33.800060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:35.324044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:36.767868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:38.221803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:41.417725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:42.954482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-01T22:05:51.256893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)Truck Transportation Employment (000's)Class 8 Truck Net Orders, US/CAN
Active Truck Utilization (SA)1.0000.4660.5740.4130.3330.4380.4220.1140.1530.3210.471
Total Truck Loadings (SA)0.4661.0000.6540.9160.5230.9290.8960.4800.0590.9600.326
Total TL: Spot Rate (exc. FSC, SA)0.5740.6541.0000.6960.1960.6310.613-0.0080.3460.6180.589
Total TL: Contract Rate (exc. FSC, SA)0.4130.9160.6961.0000.5490.9760.9720.2780.0390.9520.442
Driver Labor Index (1992=100, SA)0.3330.5230.1960.5491.0000.6190.6110.5100.1290.5220.074
Real GDP0.4380.9290.6310.9760.6191.0000.9840.3220.0260.9540.410
CPI Index0.4220.8960.6130.9720.6110.9841.0000.2810.0040.9210.395
3 Month T-Bill Rate, %0.1140.480-0.0080.2780.5100.3220.2811.000-0.0550.436-0.181
National Avg. Diesel Fuel Price ($/Gal.)0.1530.0590.3460.0390.1290.0260.004-0.0551.0000.0250.234
Truck Transportation Employment (000's)0.3210.9600.6180.9520.5220.9540.9210.4360.0251.0000.339
Class 8 Truck Net Orders, US/CAN0.4710.3260.5890.4420.0740.4100.395-0.1810.2340.3391.000

Missing values

2023-05-01T22:05:44.606933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-01T22:05:44.851769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

1Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)Truck Transportation Employment (000's)Class 8 Truck Net Orders, US/CAN
00.8836545.772625e+0797.777252100.045235116.64296715711.131909212.1739962.7533333.3081417.016185.0
10.8801835.716361e+0799.56157799.978851116.62450415687.866359212.6869962.1245003.3771413.011091.0
20.8769955.673412e+07102.66117199.975914116.73279615708.749434213.4479981.2625003.8811415.014647.0
30.8964705.692338e+07109.214226100.681190117.15164915757.289081213.9420011.2936364.0841413.014264.0
40.8912735.618877e+07114.116783101.416893117.60408015802.485668215.2079931.7338094.4251407.012060.0
50.8892635.572554e+07120.976936102.341064118.14888015818.220496217.4629971.8561904.6771396.013530.0
60.9062235.550938e+07126.923454102.990379118.48516815793.676427219.0160061.6259094.7031390.011677.0
70.8920365.441142e+07125.792679102.205620118.89601915722.518762218.6900021.7219054.3021385.012435.0
80.8530695.328081e+07118.667259102.078377119.28349315609.255104218.8769991.1342864.0241372.08677.0
90.8977265.564441e+07111.424934102.276840119.78436315476.642128216.9949950.6745453.5761368.08862.0
1Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)Truck Transportation Employment (000's)Class 8 Truck Net Orders, US/CAN
1710.9578516.577812e+07138.522766157.253036126.76912719863.026246288.6109920.7630005.1201572.013605.0
1720.9568746.590291e+07130.197922156.151688126.63966419886.814438291.2680050.9833335.5711584.012574.0
1730.9548706.567066e+07123.048965153.717255126.59146119936.254202294.7279971.4947625.7541591.012967.0
1740.9277056.549241e+07121.088226153.644455126.32007619998.635040294.6279912.2325005.4861597.09652.0
1750.9267046.516664e+07117.953949153.059692126.62178020059.780776295.3200072.6308705.0131600.019376.0
1760.9285186.547495e+07111.377747150.304672126.88344620107.270192296.5390013.1266674.9931598.050632.0
1770.8989166.546370e+07110.006798147.857254126.76727320145.297345297.9870003.7170005.2111605.039850.0
1780.8936906.522214e+07103.975731146.209427126.66890020181.456299298.5979924.1515005.2551606.032518.0
1790.8845886.417103e+07111.030983149.110367126.73442120220.685978298.9899904.2523814.7141611.025869.0
1800.8737916.500996e+07111.918747146.537552127.29487620260.513248300.5360114.5375004.5761615.018993.0